Table REF  shows the performance of deep and classic models on the synthetic images. Surprisingly, although deep models achieve state-of-the-art performance on the MIT300 benchmark, they completely fail on synthetic images and are outclassed by classical models, moreover, despite the DVA [1]} has connections between shallow layer and output layer in their model, their performance is still not good on synthetic images. Fig. REF  illustrates saliency predictions on example stimuli by the considered methods. To investigate the reason behind this failure, we looked into the effect of fine-tuning on the inner representations and neuron responses to synthetic patterns. The results in Table REF  show that deep models do indeed capture the salient patterns within the middle layers of the architecture (conv4-1 layer). Some examples (including curvature, orientation, etc) are shown in Fig. REF . Nevertheless, as indicated in Table REF , no matter how many layers are fine-tuned, the output of the deep saliency model never highlights such salient patterns. One possible reason might be that the current large databases, SALICON, are biased towards natural scenes containing daily objects (text, faces, animals, cars, etc) and do not include any image similar to synthetic patters. The models, thus, do not learn anything about simple pop-out.
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